This strategy has made great achievements in HPE [1]}, [2]}, [3]}, but it still leaves to be desired. In this strategy, feature maps are downscaled by strided convolution (or pooling). As shown in Figure REF , during the downscaling, multiple pixels on the larger feature maps are merged into the same pixel on the smaller ones. The location information will be destroyed in this process. While during the upscaling, even if the transposed convolution [4]} is used, it is hard to recover the destroyed location information. Consequently, there will be multiple possible corresponding positions on the upscaled feature maps for original single pixel. Although the final resized feature maps have the same spatial sizes, their pixels may be not well aligned. This spatial non-alignment potentially hurts the accuracy of location. Thus, it may be more preferred to fuse multi-scale features of the same spatial sizes.
